Canadian journal of public health = Revue canadienne de sante publique · 2026
Drivers of online abuse against Canadian public health officials: An LLM-based temporal analysis.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Moghaddam Samaneh Hosseini, Regehr Cheryl, Lyons Kelly, Goel Vivek, Regehr Kaitlyn
Аннотация
OBJECTIVES: This study identifies trends in abusive discourse towards public health professionals (PHPs) during the COVID-19 pandemic and explores associations between abusive digital content, case numbers, deaths, and major policy announcements.
METHODS: Natural language processing (NLP) and large language model (LLM) techniques were used to develop a computational model to detect abusive content on X (formerly Twitter). This model was applied to abusive posts targeting PHPs during COVID-19 by examining over 1.7 million posts from January 2020 to May 2021. Associations between spikes in abuse and the number of COVID-19 cases, deaths, and government monitoring updates were explored.
RESULTS: Pronounced surges in abusive posts coincided with rising case and death counts and the imposition of major federal COVID-19 policies, particularly during the pandemic's initial emergency response. Digital aggression increased at times of public health statements related to restrictions in social gatherings, testing criteria, vaccinations, and masking. However, during high-case periods, even statements providing case updates, expressing compassion, or urging collective responsibility coincided with higher numbers of abusive posts.
CONCLUSIONS: This work provides insights into the pressures faced by health officials online and offers implications for designing resilient public communication during future crises.
METHODS: Natural language processing (NLP) and large language model (LLM) techniques were used to develop a computational model to detect abusive content on X (formerly Twitter). This model was applied to abusive posts targeting PHPs during COVID-19 by examining over 1.7 million posts from January 2020 to May 2021. Associations between spikes in abuse and the number of COVID-19 cases, deaths, and government monitoring updates were explored.
RESULTS: Pronounced surges in abusive posts coincided with rising case and death counts and the imposition of major federal COVID-19 policies, particularly during the pandemic's initial emergency response. Digital aggression increased at times of public health statements related to restrictions in social gatherings, testing criteria, vaccinations, and masking. However, during high-case periods, even statements providing case updates, expressing compassion, or urging collective responsibility coincided with higher numbers of abusive posts.
CONCLUSIONS: This work provides insights into the pressures faced by health officials online and offers implications for designing resilient public communication during future crises.